A unified framework for trend uncertainty assessment in climate data records: demonstration on global mean sea level

Gobron, K., Hohensinn, R., Loizeau, X., Bulgin, C. E. ORCID: https://orcid.org/0000-0003-4368-7386, Merchant, C. J. ORCID: https://orcid.org/0000-0003-4687-9850, Woolliams, E. R., Cox, M. G., Dorigo, W., Howard, T., Langsdale, M., Povey, A. C., Ablain, M., Bogusz, J., Gruber, A., Klos, A. and Mittaz, J. (2025) A unified framework for trend uncertainty assessment in climate data records: demonstration on global mean sea level. Surveys in Geophysics. ISSN 1573-0956 (In Press)

Abstract/Summary

Trends of essential climate variables are often estimated from climate data records to quantify changes in the Earth system. An understanding of the uncertainty in a trend is essential for accurately determining the significance of a trend and attributing its causes. Despite this importance, trend-uncertainty estimates rarely account for all known sources of uncertainty. Common approaches neglect measurement-system instability or neglect the impact of natural variability on trend uncertainty. Such neglect can result in over-confidence in trend estimates. This study addresses trend-uncertainty assessment, particularly the need to account for the combined effects of measurement instability and natural variability on the trend uncertainty. The study presents a novel, unified framework for trend estimation that combines available measurement uncertainty information with empirical modelling of natural climate variability to achieve a more accurate uncertainty estimate. The framework is demonstrated for a time series of global mean sea level observations, obtaining more realistic trend-uncertainty values. The framework is applicable to most other climate data records. Adopting this approach will enhance confidence in climate change analysis through more accurate trend-uncertainty assessment in climate studies.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/127602
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Springer
Download/View statistics	View download statistics for this item